Oxford VGGFace Implementation using Keras Functional Framework 1.1
- This model is converted from original caffe network. (580 MB)
- It supports both Tensorflow and Theano backeds.
- You can also load only feature extraction layers with VGGFace(include_top=False) initiation (59MB).
- When you use it for the first time , weights are downloaded and stored in ~/.keras folder.
- Keras v1.1
- Tensorflow v10
- Theano v0.8.2
- Tensorflow backend with 'tf' dimension ordering
from scipy import misc
import copy
import numpy as np
from vggface import VGGFace
model = VGGFace()
im = misc.imread('../image/ak.jpg')
im = misc.imresize(im, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.expand_dims(im, axis=0)
res = model.predict(im)
print np.argmax(res[0])- Theano backend with 'th' dimension ordering
from scipy import misc
import copy
import numpy as np
from vggface import VGGFace
model = VGGFace()
im = misc.imread('../image/ak.jpg')
im = misc.imresize(im, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.transpose(im, (2, 0, 1))
im = np.expand_dims(im, axis=0)
res = model.predict(im)
print np.argmax(res[0])###Licence
Original models can be used for non-commercial research purposes under Creative Commons Attribution License.
The code that provided in this project is under MIT License.
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